@inproceedings{liu-etal-2019-fast,
title = "Fast Prototyping a Dialogue Comprehension System for Nurse-Patient Conversations on Symptom Monitoring",
author = "Liu, Zhengyuan and
Lim, Hazel and
Suhaimi, Nur Farah Ain and
Tong, Shao Chuen and
Ong, Sharon and
Ng, Angela and
Lee, Sheldon and
Macdonald, Michael R. and
Ramasamy, Savitha and
Krishnaswamy, Pavitra and
Chow, Wai Leng and
Chen, Nancy F.",
editor = "Loukina, Anastassia and
Morales, Michelle and
Kumar, Rohit",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-2004",
doi = "10.18653/v1/N19-2004",
pages = "24--31",
abstract = "Data for human-human spoken dialogues for research and development are currently very limited in quantity, variety, and sources; such data are even scarcer in healthcare. In this work, we investigate fast prototyping of a dialogue comprehension system by leveraging on minimal nurse-to-patient conversations. We propose a framework inspired by nurse-initiated clinical symptom monitoring conversations to construct a simulated human-human dialogue dataset, embodying linguistic characteristics of spoken interactions like thinking aloud, self-contradiction, and topic drift. We then adopt an established bidirectional attention pointer network on this simulated dataset, achieving more than 80{\%} F1 score on a held-out test set from real-world nurse-to-patient conversations. The ability to automatically comprehend conversations in the healthcare domain by exploiting only limited data has implications for improving clinical workflows through red flag symptom detection and triaging capabilities. We demonstrate the feasibility for efficient and effective extraction, retrieval and comprehension of symptom checking information discussed in multi-turn human-human spoken conversations.",
}
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<abstract>Data for human-human spoken dialogues for research and development are currently very limited in quantity, variety, and sources; such data are even scarcer in healthcare. In this work, we investigate fast prototyping of a dialogue comprehension system by leveraging on minimal nurse-to-patient conversations. We propose a framework inspired by nurse-initiated clinical symptom monitoring conversations to construct a simulated human-human dialogue dataset, embodying linguistic characteristics of spoken interactions like thinking aloud, self-contradiction, and topic drift. We then adopt an established bidirectional attention pointer network on this simulated dataset, achieving more than 80% F1 score on a held-out test set from real-world nurse-to-patient conversations. The ability to automatically comprehend conversations in the healthcare domain by exploiting only limited data has implications for improving clinical workflows through red flag symptom detection and triaging capabilities. We demonstrate the feasibility for efficient and effective extraction, retrieval and comprehension of symptom checking information discussed in multi-turn human-human spoken conversations.</abstract>
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%0 Conference Proceedings
%T Fast Prototyping a Dialogue Comprehension System for Nurse-Patient Conversations on Symptom Monitoring
%A Liu, Zhengyuan
%A Lim, Hazel
%A Suhaimi, Nur Farah Ain
%A Tong, Shao Chuen
%A Ong, Sharon
%A Ng, Angela
%A Lee, Sheldon
%A Macdonald, Michael R.
%A Ramasamy, Savitha
%A Krishnaswamy, Pavitra
%A Chow, Wai Leng
%A Chen, Nancy F.
%Y Loukina, Anastassia
%Y Morales, Michelle
%Y Kumar, Rohit
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F liu-etal-2019-fast
%X Data for human-human spoken dialogues for research and development are currently very limited in quantity, variety, and sources; such data are even scarcer in healthcare. In this work, we investigate fast prototyping of a dialogue comprehension system by leveraging on minimal nurse-to-patient conversations. We propose a framework inspired by nurse-initiated clinical symptom monitoring conversations to construct a simulated human-human dialogue dataset, embodying linguistic characteristics of spoken interactions like thinking aloud, self-contradiction, and topic drift. We then adopt an established bidirectional attention pointer network on this simulated dataset, achieving more than 80% F1 score on a held-out test set from real-world nurse-to-patient conversations. The ability to automatically comprehend conversations in the healthcare domain by exploiting only limited data has implications for improving clinical workflows through red flag symptom detection and triaging capabilities. We demonstrate the feasibility for efficient and effective extraction, retrieval and comprehension of symptom checking information discussed in multi-turn human-human spoken conversations.
%R 10.18653/v1/N19-2004
%U https://aclanthology.org/N19-2004
%U https://doi.org/10.18653/v1/N19-2004
%P 24-31
Markdown (Informal)
[Fast Prototyping a Dialogue Comprehension System for Nurse-Patient Conversations on Symptom Monitoring](https://aclanthology.org/N19-2004) (Liu et al., NAACL 2019)
ACL
- Zhengyuan Liu, Hazel Lim, Nur Farah Ain Suhaimi, Shao Chuen Tong, Sharon Ong, Angela Ng, Sheldon Lee, Michael R. Macdonald, Savitha Ramasamy, Pavitra Krishnaswamy, Wai Leng Chow, and Nancy F. Chen. 2019. Fast Prototyping a Dialogue Comprehension System for Nurse-Patient Conversations on Symptom Monitoring. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers), pages 24–31, Minneapolis, Minnesota. Association for Computational Linguistics.